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/*
* Copyright (c) 2017-2019 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to
* deal in the Software without restriction, including without limitation the
* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
* sell copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all
* copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
#ifndef ARM_COMPUTE_CLWEIGHTSRESHAPEKERNEL_H
#define ARM_COMPUTE_CLWEIGHTSRESHAPEKERNEL_H
#include "arm_compute/core/CL/ICLKernel.h"
namespace arm_compute
{
/** OpenCL kernel to perform reshaping on the weights used by convolution and locally connected layer
*
* Rearranges each 3-dimensional kernel to a single row leading to a matrix with linearized kernels.
* In combination with the @ref CLIm2ColKernel can transform a convolution to a matrix multiplication.
*
* For example assuming a 3D weight kernel of 3x3 dimensions and depth of 2 we have:
* @f[
* \left( \begin{array}{ccc}
* a000 & a001 & a002 \\
* a010 & a011 & a012 \\
* a020 & a021 & a022 \\
* \end{array} \right)
* \left( \begin{array}{ccc}
* a100 & a101 & a102 \\
* a110 & a111 & a112 \\
* a120 & a121 & a122 \\
* \end{array} \right)
* \rightarrow
* \left( \begin{array}{ccccccccc}
* a000 & a001 & a002 & a010 & a011 & a012 & a020 & a021 & a022 & a100 & a101 & a102 & a110 & a111 & a112 & a120 & a121 & a122 \\
* \end{array} \right)
* @f]
*/
class CLWeightsReshapeKernel : public ICLKernel
{
public:
/** Constructor.*/
CLWeightsReshapeKernel();
/** Prevent instances of this class from being copied (As this class contains pointers) */
CLWeightsReshapeKernel(const CLWeightsReshapeKernel &) = delete;
/** Prevent instances of this class from being copied (As this class contains pointers) */
CLWeightsReshapeKernel &operator=(const CLWeightsReshapeKernel &) = delete;
/** Allow instances of this class to be moved */
CLWeightsReshapeKernel(CLWeightsReshapeKernel &&) = default;
/** Allow instances of this class to be moved */
CLWeightsReshapeKernel &operator=(CLWeightsReshapeKernel &&) = default;
/** Default destructor */
~CLWeightsReshapeKernel() = default;
/** Set the input and output of the kernel.
*
* @param[in] input The input tensor to convert. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM] if shared,
* and 5D tensor with dimensions [kernel_x, kernel_y, IFM, OFM, num_patches] if unshared. Data types supported: All
* @param[in] biases The shared biases tensor to append. Bias is 1D tensor with dimensions [OFM] if shared and 2D tensor with
* dimensions [OFM, num_patches] if unshared. Data types supported: F16/F32, for quantized types this must be nullptr.
* @warning Appending biases to weights reshaped matrix is not supported for quantized asymmetric types.
* @param[out] output The output tensor. Should be a 2D Tensor if there are no groups and the weights are not shared; a 3D Tensor otherwise.
* Data types supported: Same as @p input
* @param[in] num_groups (Optional) Number of groups when performing a grouped convolution. num_groups != 1 is only supported for NCHW data layout
* Number of groups greater than one are only supported for NCHW data layout, and the number of weights must be a multiple of it.
*/
void configure(const ICLTensor *input, const ICLTensor *biases, ICLTensor *output, unsigned int num_groups = 1);
/** Static function to check if given info will lead to a valid configuration of @ref CLWeightsReshapeKernel
*
* @param[in] input The input tensor to convert. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM] if shared,
* and 5D tensor with dimensions [kernel_x, kernel_y, IFM, OFM, num_patches] if unshared. Data types supported: All
* @param[in] biases The shared biases tensor to append. Bias is 1D tensor with dimensions [OFM] if shared and 2D tensor with
* dimensions [OFM, num_patches] if unshared. Data types supported: F16/F32, for quantized types this must be nullptr.
* @warning Appending biases to weights reshaped matrix is not supported for quantized asymmetric types.
* @param[in] output The output tensor. Should be a 2D Tensor if there are no groups and the weights are not shared; a 3D Tensor otherwise.
* Data types supported: Same as @p input
* @param[in] num_groups (Optional) Number of groups when performing a grouped convolution. num_groups != 1 is only supported for NCHW data layout
* Number of groups greater than one are only supported for NCHW data layout, and the number of weights must be a multiple of it.
*
* @return a status
*/
static Status validate(const ITensorInfo *input, const ITensorInfo *biases, const ITensorInfo *output, unsigned int num_groups = 1);
// Inherited methods overridden:
void run(const Window &window, cl::CommandQueue &queue) override;
private:
const ICLTensor *_input;
const ICLTensor *_biases;
ICLTensor *_output;
};
} // namespace arm_compute
#endif /*ARM_COMPUTE_CLWEIGHTSRESHAPEKERNEL_H */
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